Contactless and minimal-contact monitoring of quality of life parameters for assessment and intervention

ABSTRACT

An apparatus, system, and method for the measurement, aggregation and analysis of data collected using non-contact or minimally-contacting sensors provides quality of life parameters for individual subjects, particularly in the context of a controlled trial of interventions on human subjects (e.g., a clinical trial of a drug, or an evaluation of a consumer item such as a fragrance). In particular, non-contact or minimal-contact measurement of quality-of-life parameters such as sleep, stress, relaxation, drowsiness, temperature and emotional state of humans may be evaluated, together with automated sampling, storage, and transmission to a remote data analysis center. One component of the system is that the objective data is measured with as little disruption as possible to the normal behavior of the subject. The system can also support behavioral and pharmaceutical interventions aimed at improving quality of life.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/120,640, filed Mar. 23, 2011, which claims benefit toPCT/US2009/058020 filed Sep. 23, 2009, which claims benefit of U.S.Provisional Application 61/099,792 filed Sep. 24, 2008, the disclosuresof which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

This disclosure relates to the measurement, aggregation and analysis ofdata collected using non-contact or minimal-contact sensors togetherwith a means for capturing subjective responses to provide quality oflife parameters for individual subjects, particularly in the context ofa controlled trial of interventions on human subjects (e.g., a clinicaltrial of a drug, or an evaluation of a consumer item such as afragrance).

Monitoring of quality-of-life (QOL) parameters can be of importance whendeveloping interventions aimed at improving a person's QOL.Quality-of-life parameters are measurements of general well-being whichare generally accepted as being meaningful to an individual's perceptionof their life. In general QOL markers have a combination of anunderlying objectively measurable elements, and a subjectively relatedelement. Specific non-limiting examples include:

-   -   Sleep quality—an individual can subjectively report whether they        are sleeping well or badly, and this has an impact on their        perceived QOL. For a sleep quality QOL parameter, an objective        measurement could be sleep duration, and a subjective input        could be “how restful” the sleep was.    -   Stress—an individual can report on whether they find their        current life circumstances to be stressful. For a stress QOL        parameter, an objective measurement could be heart rate or        cortisol levels; a subjective element could be a stress level        questionnaire    -   Relaxation—an individual can report the subjective sensation of        being relaxed, which can also be objectively related to        autonomic nervous system activity.    -   Pain—an individual can subjectively record levels of pain using        a Pain Index [such as the Visual Analog Scale]. More objective        measurements of pain can be obtained using a dolorimeter.    -   Body temperature—subjects can often report feelings of        overheating or coolness which are not directly related to        objective measurement of body core temperature.    -   Vigilance/drowsiness—vigilance, or attentiveness can also be        measured objectively (e.g., using the psychomotor vigilance        test) or through subjective questionnaires.

For clarification, a non-contact (or contactless) sensor is one whichsenses a parameter of a subject's physiology or behavior without anydirect physical contact with a subject. Non-limiting examples include amovement detector based on radio-wave reflections, a microphone placedremotely from a subject, an infrared camera recording the surfacetemperature, or a faucet-monitor which records turning on of a faucet towash hands. A minimal contact sensor may be considered to be one inwhich there is some physical contact with a sensor, but this is limitedto short durations. Examples include a weight scale, a blood pressuremonitor, a breath analyzer, or a hand-held surface ECG monitor. Theseminimal contact sensors can be distinguished from contact sensorstypically used in clinical trials such as ECG patches, oximeters, EEGelectrodes, etc, where there typically is adhesion to the body, andtypically the sensor is intended for use over prolonged periods of time(e.g. >1 hour).

A key unifying factor in defining QOL parameters is the need to combineobjective data from sensors, and subjective data from the monitoredsubject to assess the overall QOL. A particular challenge then ariseswhen one wishes to measure the impact of an intervention on changes inQOL. For example, a company who has developed a drug to counteract sleepdisruption will be interested to see if its drug has had any directimpact on a person's sleep which has resulted in either objectively orsubjectively improved QOL. Similarly if a company has developed aproduct such as a skin emollient to reduce itchiness due to dry skin,they may wish to see if there has been an improved QOL (i.e., reducedscratching, lower level of discomfort) etc.

One commonly accepted means for answering such questions is to conduct aclinical or consumer trial which poses a statistical hypothesis whichcan be verified or rejected with a certain level of confidence. Forexample, in drug trials a double-blinded random controlled trial is awell accepted methodology for ascertaining the effect of drugs. However,measurement of QOL is difficult to conduct for a number of reasons,which various aspects of this disclosure can overcome: (a) it can bedifficult to define a suitable measure for a QOL outcome, (b) by wearinga measurement device to measure QOL, one may directly impact on theexact quality-of-life parameter you wish to study, (c) there arelogistical and financial challenges of measuring parameters in a natural“home” setting rather than in a formal laboratory setting. There are avariety of conventional techniques to measure some aspects of QOL whichwill now be discussed, together with their limitations.

Monitoring of a quality-of-life parameter can be motivated by a desireto integrate it into an intervention program. As an example, a personmay undertake cognitive behavioral therapy (CBT) to reduce theirstress-related quality of life. An important component of a CBT programis the ongoing assessment of the stress quality of life index, whosemeasurement will itself form part of the behavioral intervention. As asecond example of an embodiment of the disclosure, we will describe asystem for improving sleep quality through use of objective andsubjective measurements of sleep quality-of-life indices.

As specific examples of the limitations of the current state of the art,consider the problem of measuring sleep quality in response to ananti-insomnia drug. Firstly, defining “sleep quality” as it relates toquality of life can be difficult, as this will often be a combination ofobjective and subjective measurements. Secondly, the current methodfavored for measuring sleep is to use a so-called polysomnogram whichmeasures multiple physiological parameters (EEG, ECG, EOG, respiratoryeffort, oxygen level etc.). While the resulting physiologicalmeasurements are very rich, their measurement fundamentally alters thesleeping state of the subject (e.g., it is harder for them to tum overin bed), and cannot represent a true QOL sleep measurement. Finally, thecurrent cost of the polysomnogram test (approximately $1500 in 2008)makes it an impractical tool for measurement of sleep quality in largenumbers of subjects over long periods of time. Accordingly, there is aneed for a system which can provide robust measurements of sleepquality-of-life in a highly non-invasive fashion. In an embodiment ofour system, we describe one method for objectively measuring sleepquality using a totally non-invasive biomotion sensor. This can becombined with a number of subjective tools for measuring sleep quality,such as the Pittsburgh Sleep Quality Index and the Insomnia SeverityIndex (these consist of questionnaires on sleep habits such astime-to-bed, estimated time-to-fall-asleep etc.

Another QOL parameter of interest is stress level or, conversely,relaxation. Current techniques for objective measurement of stressinclude measurement of heart rate variability or cortisol levels.However, measurement of heart rate variability typically requires thesubject to wear electrodes on the chest, which is often impractical forsituations of daily living. Likewise, collection of cortisol samples toassess stress requires frequent collection of saliva samples, and isdifficult to integrate into a daily living routine. There are also anumber of widely used subjective measurements of stress or anxiety(e.g., Spielberger's State-Trait Anxiety Inventory). Accordingly, amethod, system or apparatus which can reliably gather information aboutstress-related QOL parameters would have utility in a variety ofsettings.

Finally, measurement of the quality-of-life implications of chronic pain(such as chronic lower back pain) would have utility for assessing thebenefit of therapies, or for providing cognitive feedback on painmanagement. Current subjective measurement tools such as the OswestryDisability Index and the 36-Item Short-Form Health Survey are used toassess subjective quality of life in subjects with chronic painconditions. Objective measurements of pain are not well defined, butthere is some evidence that heart rate is correlated with painintensity.

Accordingly, there is a clearly established need for systems and methodswhich measure quality-of-life outcomes in ambulatory/home settings, andwhich have minimal impact on the daily routine of the person whose QOLis being monitored. This is a particular need in clinical trials fornon-contact or minimal contact sensors where the effects ofinterventions such as drugs, ointments, physiotherapy, nutriceuticals,behavior changes etc. are being evaluated.

SUMMARY

This disclosure provides various embodiments of an apparatus, system,and method for monitoring of quality-of-life parameters of a subject,using contact-free or minimal-contact sensors, in a convenient andlow-cost fashion. The typical user of the system is a remote observerwho wishes to monitor the QOL of the monitored subject in asnon-invasive fashion as possible. The system typically includes: (a) oneor more contactless or minimal-contact sensor units suitable for beingplaced close to where the subject is present (e.g., on a bedside table),(b) an input device for electronically capturing subjective responsessuch as a cell-phone, PDA etc., (c) a device for aggregating datatogether and transmitting to a remote location, (d) a display unit forshowing information to the local user, and (e) a data archiving andanalysis system for display and analysis of the quality-of-lifeparameters. The component (e) can also be used as a feedback device forinterventional programs. For convenience, the sensor unit, input unit,data aggregation/transmission unit, and the display/monitoring unit canbe incorporated into a single stand-alone unit, if desired (for example,all of these functions could be integrated on a cell-phone platform).The sensor units may include one or more of a non-contact measurementsensor (for detection of parameters such as sound, general bodilymovement, respiration, heart rate, position, temperature), and one ormore minimal contact sensors (e.g. weighing scales, thermometer). In oneor more aspects of this disclosure, a system may incorporate aprocessing capability (which can be either at the local or remote sites)to generate quality-of-life parameters based on the objective andsubjective measurements from a user. As a specific example, an overallsleep quality-of-life could be generated by combining the subjectiveresponse of the user to the Insomnia Severity Index together with anobjective measurement of sleep duration.

In one or more embodiments, the disclosed approaches (pharmaceutical,device-based or behavioral) are useful in improving the quality-of-lifeparameters for these subjects. In particular, non-contact orminimal-contact measurement of quality-of-life parameters such as sleep,stress, relaxation, drowsiness, temperature and emotional state ofhumans is disclosed, together with means for automated sampling,storage, and transmission to a remote data analysis center. In one ormore embodiments, one aspect of the system measures objective data withas little disruption as possible to the normal behavior of the subject.

In one particular embodiment, a quality-of-life monitoring system forhuman subjects, includes a plurality of multi-parameter physiologicaland environmental sensors configured to detect a plurality ofphysiological and environmental parameters related to a quality of lifeassessment, wherein each of said plurality of sensors either have nocontact or minimal contact with a monitored subject; a timer thatcontrols sampling of the detected parameters and allows a chronologicalreconstruction of recorded signals relating thereto; an input devicewhich captures subjective responses from the monitored subject; a datastorage device configured to record sampled signals; a data transmissioncapability so that data collected from a subject can be transmitted to aremote data monitoring center, and messages can be transmitted to themonitoring sensors; and a data monitoring and analysis capability sothat overall quality-of-life parameters can be calculated based on themeasured signals.

In another embodiment, a method for assessing a quality of life indexincludes measuring multi-parameter physiological and environmentalparameters which are related to a quality of life assessment of amonitored subject with no contact or minimal contact with the monitoredsubject; collecting subjective responses from the monitored subjectabout their quality-of-life; analyzing objective and subjectivemeasurements to generate a quantitative quality-of-life index; andgenerating suggested interventions to affect the measured quality oflife index of the monitored subject.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure will now be described with reference tothe accompanying drawings in which:

FIG. 1 is a diagram illustrating an overall schematic of an embodiment;

FIG. 2 is a specific example of an embodiment in which a contactlesssensor is used to monitor the sleeping state of a subject, by placementin a nearby location (bedside locker);

FIG. 3 is an example of an input device embodiment that could be used tocapture subjective responses from individuals;

FIG. 4 is an alternative example of an embodiment in which a web-sitecould be used to capture the subjective responses from an individual;

FIGS. 5A and B show representations of some of the raw data captured bya specific contactless sensor used in a sleep trial based on anembodiment of this disclosure;

FIG. 6 shows example results of the system in measuring sleep apnea inclinical trial; and

FIGS. 7A and 7B show schematic representations of behavioralinterventions based on one or more embodiments of this disclosure.

DETAILED DESCRIPTION

FIG. 1 is a diagram illustrating an overall schematic of an embodimentof this disclosure. Monitored subject 101 may be observed by a pluralityof contactless 102 and minimal contact sensors 103. Subject 101 may alsohas access to input device 104 capable of obtaining subjective feedbackfrom the subject through written text or recorded sound. Dataaggregation and transmission device 105 collects the data from thesensors 102, 103 and 104, and may also control data sampling and inputparameters used by the various sensors and devices. Optionally,display/feedback device 107 can be provided to the local user (e.g.,this might indicate whether a signal is being collected from them, orgive feedback on the most recent set of QOL parameters measured). Dataaggregation and transmission device 105 may be configured to communicatein a bilateral way with remote data archiving and analysis system 106.Data archiving and analysis system 106 may store data from a pluralityof subjects, and can carry out analysis of the recorded signals andfeedback. It may also communicate with data display device 107 which canshow the results of the analysis to a user, or with an optional separatedisplay device 108 which shows the QOL parameters to a remote user.

FIG. 2 illustrates an embodiment of a contactless sensor thatobjectively monitors the sleeping state of a subject. In thisembodiment, the sensor unit may contain one or more of a radio-frequencybased biomotion sensor, a microphone (to pick up ambient sound), atemperature sensor (to pick up ambient temperature), a light sensor (topick up ambient light levels), and an infrared detector for measuringthe subject temperature. The contactless sensor may be placed on abedside table, for example.

FIG. 3 illustrates an example of an embodiment of an input device forcollecting user input. The input device would typically includealphanumeric keypad 301, display 302, microphone 303, and loudspeaker304. This allows the generation of questions using either visual oraudio means, and a person can then answer the questions using eithertext or audio input.

FIG. 4 illustrates an embodiment using a personal computer with aninternet browser to capture subjective perceptions of sleep.

FIGS. 5A and 5B provide examples of raw signals captured using acontactless sensor in a trial for measuring sleep quality-of-life. FIG.4A shows the signal when a person is asleep and then turns over on theirside. FIG. 4B shows the signal when the person is in deep sleep.

FIG. 6 is an example of how the contactless system can estimateapnea-hypopnea index in a clinical trial with an accuracy similar tothat of the current polysomnogram (PSG) estimates.

FIG. 7 is an example of a behavioral intervention based on use of thesystem to enhance sleep quality. FIG. 7(A) shows the components of aintervention based over several weeks, in which there is an initialsession at which detailed information about sleep is provided, and theperson is given the system for measurement of their sleepquality-of-life index (SQOLI).

FIG. 7(B) shows an example of a specific algorithm that could be usedwithin the intervention, based on the feedback from the SQOLImonitoring. For example, if they achieve an SQOLI greater than target,they can increase their time in bed by 30 minutes. If they fail, theycan reduce time in bed by 15 minutes.

A typical embodiment of a system of this disclosure may include one ormore non-contact sensors or minimal-contact sensors that can include oneor more of the following:

-   -   (a) A biomotion sensor which measures movement, and which        derives respiration, heart rate and movement parameters. An        example of such a sensor is more fully described in the article        written by P. de Chazal, E. O'Hare, N. Fox, C. Heneghan,        “Assessment of Sleep/Wake Patterns Using a Non-Contact Biomotion        Sensor”, Proc. 30th IEEE EMBS Conference, Aug 2008, published by        the IEEE, the entire contents of which are incorporated herein        by reference. In one embodiment, the biomotion sensor may use a        series of radio-frequency pulses at 5.8 GHz to generate echoes        from a sleeping subject. The echoes may be mixed with the        transmitted signals to generate a movement trace which includes        movements due to breathing, heart rate, and positional changes.    -   (b) An audio sensor which measures ambient sound. A specific        example of a microphone appropriate for inclusion in the system        would be the HK-Sound Omni, −27 dB microphone with part number        S-0M9765C273S-C08.    -   (c) A temperature sensor which measures environmental        temperature (typically to ±1C). A specific example of a        temperature sensor appropriate for inclusion would be the        National Semiconductor LM20, SC70 package.    -   (d) A light level sensor would measure light level. A specific        example of a light level sensor appropriate for inclusion is the        Square n·:E· Clipsal Light-Level Sensor.    -   (e) A body-temperature measuring sensor. A specific example of a        sensor that may be used in the system is the body thermometer        Part No. 310 from the YuanYa Far Asia Company.

The minimal contact sensors may include one or more of the following:

-   -   (a) A weighing scales for measuring body weight. A specific RST        3.3-005 CONexample is the A&D UC-321PBT.    -   (b) A blood pressure device, such as the A&F UA767PBT.    -   (c) A continuous positive airway pressure device for treating        sleep apnea, such as the ResMed Autoset Spirit S8.    -   (d) A pedometer for measuring step-counts (such as the Omron        Pocket Pedometer with PC software, HJ-7201TC).    -   (e) A body-worn accelerometer for measuring physical activity        during the day (such as the ActivePAL device).    -   (f) A body composition analyzer such as the Omron Body        Composition Monitor with Scale, HBF-500, which calculates        visceral fat and base metabolic rate.    -   (g) Other contactless or minimally contacting devices could also        be included.

In one or more embodiments, the system may include a data-acquisitionand processing capability which provides a logging capability for thenon-contact and minimal- contact sensors described above. This typicallycould include, for example, an analog-to-digital converter (ADC), atimer, and a processor. The processor may be configured to control thesampling of the signals, and may also apply any necessary dataprocessing or reduction techniques (e.g., compression) to minimizeunnecessary storage or transmission of data.

A data communication subsystem may provide communication capabilitywhich could send the recorded data to a remote database for furtherstorage and analysis, and a data analysis system including, for example,a database, can be configured to provide processing functionality aswell as input to a visual display.

In one specific embodiment of the system, data acquisition, processing,and communications can utilize using, for example, a Bluetooth-enableddata acquisition device (e.g. the commercially available BlueSentry®device from Roving Networks). Other conventional wireless approaches mayalso be used. This provides the ability to sample arbitrary voltagewaveforms, and can also accept data in digital format.

In this embodiment, the Bluetooth device can then transmit data to acell phone using the Bluetooth protocol, so that the data can be storedon a cell-phone memory. The cell phone can also carry out initialprocessing of the data. The cell phone can also be used as a device forcapturing subjective data from the user, using either a text-based entrysystem, or through a voice enabled question-and-answer system.Subjective data can also be captured using a web-page.

The cell phone can provide the data transmission capability to a remotesite using protocols such as GPRS or EDGE. The data analysis system is apersonal computer running a database (e.g., the My SQL databasesoftware), which is capable of being queries by analysis software whichcan calculate useful QOL parameters. Finally a data display capabilitycan be provided by a program querying the database, the outputs of theanalytical program and using graphical or text output on a web browser.

As an example of the clinical use of a specific embodiment, the systemwas used to measure quality-of-life related to sleep in a specificclinical trial scenario. A group of 15 patients with chronic lower backpain (CLBP), and an age and gender matched cohort of 15 subjects with noback pain were recruited. After initial screening and enrollment, studyparticipants completed a baseline assessment. Gender, age, weight,height, BMI and medication usage were recorded. All subjects completedbaseline self report measures of sleep quality (Pittsburgh Sleep QualityIndex Insomnia Severity Index [16], quality of life (SF36v2) [17] andpain as part of the SF36v2 questionnaire (bodily pain scale of theSF36v2). The CLBP subjects also completed the Oswestry Disability Index(ODI) as a measure of functional disability related to their low backpain. All subjects then underwent two consecutive nights of objectivemonitoring using the non-contact biomotion sensor mentioned above, whilesimultaneously completing a subjective daily sleep log; the PittsburghSleep Diary. Table 1 shows some objective measurements of sleep usingthe system, and includes the total sleep time, sleep efficiency, sleeponset latency. Other objective parameters which could be measured wouldinclude: number of awakenings (>1 minute in duration) andwake-after-sleep-onset.

TABLE 1 Objective sleep indices obtained using the system Control GroupCLBP Group Variable (mean ± sd) (mean ± sd) p-value Total sleep time(mins) 399 (41) 382 (74) 0.428 Sleep Efficiency (%) 85.8 (4.4) 77.8(7.8) 0.002 Sleep Latency (mins)  9.4 (10.2)  9.3 (11.1) 0.972

The objective sleep indices described in Table 1 were obtained using asleep stage classification system that processed the non-contactbiomotion sensor data to produce sleep and awake classifications every30 seconds. This was developed using the following observations:

Large movements (e.g., several em in size) can be easily recognized inthe non-contact signal. Bodily movement provides significant informationabout the sleep state of a subject, and has been widely used inactigraphy to determine sleep/wake state. The variability of respirationchanges significantly with sleep stage. In deep sleep, it has long beennoted that respiration is steadier in both frequency and amplitude thanduring wakefulness of REM sleep.

Accordingly, a first stage in processing of the non-contact biomotionsignal was to identify movement and respiration information. Toillustrate how this is possible, FIG. 4A shows an example of the signalrecorded by the non-contact sensor when there is a significant movementof the torso and arms due to the person shifting sleeping position. Analgorithm based on detection of high amplitude and frequency sections ofthe signal was used to isolate the periods of movement.

For periods where there is no significant limb or torso movement,respiratory-related movement is the predominant recorded signal andestimates of breathing rate and relative amplitude are obtained using apeak and trough identifying algorithm. FIG. 4B illustrates the signalrecorded by the sensor during a period of Stage 4 sleep thatdemonstrates a steady breathing effort.

To validate the performance of the system in correctly labeling30-second epochs, we recorded signals simultaneously with a fullpolysomnogram (PSG) montage. We compared the sleep epoch annotationsfrom the PSG and the non-contact biomotion sensor and report the overallclassification accuracy, sleep sensitivity and predictivity, wakespecificity and predictivity. The overall accuracy is the percentage oftotal epochs correctly classified. The results are shown in Table 2, andprovide evidence that the system can objectively measure sleep with ahigh degree of accuracy.

TABLE 2 Accuracy of objective recognition of sleep state using thecontactless method Overall By sleep state Awake 69% Awake 69% Sleep 87%REM 82% Pred. of Awake 53% Stage 1 61% Pred. of Sleep 91% Stage 2 87%Accuracy 82% Stage 3 97% Stage 4 98%

Table 3 shows some of the subjective measurements from the samesubjects, and includes their subjective assessment of sleep duration,sleep efficiency, number of awakenings, and sleep latency for eachnight, as well as their overall PSQI and 1SI scores.

TABLE 3 Subjective sleep indices obtained using the system Control GroupCLBP Group Variable (mean ± sd) (mean ± sd) p-value Pittsburgh SleepQuality Index 2.1 (2.1) 11.7 (4.3)  <0.001 Insomnia Severity Index 2.8(4.6) 13.4 (7.3)  <0.001 Estimated Sleep Onset Latency 11.7 (4.3)  45.3(27.7) <0.001 Estimated Sleep Efficiency 95.3 (5.8)  73.4 (16.5) <0.001Estimated Night Time 2/15 15/ <0.001 Awakenings

The system can report these subjective and objective measurements ofsleep but, in one aspect, it can also report parameters related tooverall Sleep Quality of Life Index (SQOLI) which combines objective andsubjective measurements. There are a number of ways in which this couldbe done. For example, we could define the following SQOL indices:

-   -   SQOL duration={0.8×OBJECTIVE SLEEP DURATION+0.2xOBJECTIVE SLEEP        DURATION}    -   SQOL fragmentation={(number of periods of objectively measured        wakefulness>1 minute+reported self awakenings/objective sleep        duration}

${{SQOL}\mspace{14mu} {latency}} = \sqrt{{OBJECTIVE}\mspace{14mu} {SLEEP}\mspace{14mu} {LATENCY} \times {SUBJECTIVE}\mspace{14mu} {SLEEP}\mspace{14mu} {LATENCY}}$

The skilled user will be able to construct other combined measurementsof sleep quality of life which capture the most meaningful outcomes fora particular application.

In another embodiment, the system may be used to capture quality-of-lifein patients with chronic cough (e.g., patients suffering from chronicobstructive pulmonary disease). In this embodiment, two contactlesssensors may be used: the non-contact biomotion sensor described above,and a microphone. The system can measure objectively sounds associatedwith each coughing episode, and the respiratory effort associated witheach cough. This provides a more accurate means of collecting coughfrequency than relying on sound alone. There are also subjectivemeasurements of cough impact on quality of life (e.g., the parentcough-specific QOL (PC-QOL) questionnaire described in “Development of aparent-proxy quality-of-life chronic cough-specific questionnaire:clinical impact vs psychometric evaluations,” Newcombe P A, Sheffield JK, Juniper E F, Marchant J M, Halsted R A, Masters I B, Chang A B,Chest. 2008 February; 133(2):386-95).

As another exemplary embodiment, the system could be used as a screeningtool to identify sleep apnea severity and incidence in a clinical trialsetting. In this embodiment, the contactless biomotion sensor is used todetect periods of no-breathing (apnea) and reduced amplitude breathing(hypopnea). FIGS. 5A and 5B show the estimated sleep apnea severity ofthe patients enrolled in a clinical trial, prior to therapy, as anexample of how the system can be used.

The user skilled in the art will realize that the system can be used ina number of clinical trial settings where measurement of quality-of-lifeis important. As specific examples of such uses, we can consider:

Measurement of sleep quality of life in patients with atopic dermatitis(AD). Subjects with AD often have poor quality of life due to daytimeitchiness combined with poor sleep quality due to subconsciousscratching during sleep. In a clinical trial designed to assess theimpact of an intervention such as a new drug or skin-cream, the systemcan be used to capture subjective and objective quality of lifeparameters as a final outcome measure. The outcome of the sleepquality-of-life index measurement can be a recommendation on whether touse a certain active medication, and the dosage of that medication.

Measurement of sleep quality in infants in response to feeding products.For example, lactose intolerance is known to affect quality-of-life inbabies due to disrupted sleep, stomach pain, and crying episodes.Feeding products which aim to overcome lactose intolerance can beassessed by combination of objective sleep indices plus parent-reportedcrying episodes, to form an overall quality-of-life index.

As a further specific embodiment, sleep quality can be enhanced byproviding a behavioral feedback program related to sleep quality oflife. A person self-reporting insomnia can use the system as follows toenhance their sleep quality of life.

On a first visit with a physician, a person can self-report generaldissatisfaction with their sleep quality of life. They can then chooseto undertake a cognitive behavioral therapy program in the followingsteps.

Step 1: They undertake an induction session with a therapist orself-guided manual. In this induction step, the individual is introducedto information about basic physiological mechanisms of sleep such asnormal physiological sleep patterns, sleep requirements, etc. This stepensures there are no incorrect perceptions of sleep (i.e. a personbelieving that 3 hours sleep a night is typical, or that you must sleepexactly 8 hrs per day for normal health).

Step 2: Bootzin stimulus control instructions. In this step,subject-specific information is established, and basic behavioralinterventions are agreed. For example, firstly, the subject andtherapies agree a target standard wake-up time (e.g., 7 AM). They thenagree behavioral interventions such as getting out of bed after 20minutes of extended awakening, and the need to avoid sleep-incompatiblebedroom behavior (e.g., television, computer games, . . . ). They mayagree to eliminate daytime naps.

Step 3: Establish initial target. Based on discussions above, thepatient and therapist may then agree a sleep quality of life index(SQOLI) which will act as a target. As a specific example, the SQOLI maybe based on achieving 85% sleep efficiency and a subjective “difficultyfalling asleep” rating of <5 (on a 1-10.scale where 10 is very difficultand 1 is easy) The behavioral program will then consist of a week inwhich the patient tries to achieve the target based on going to bed 5hours before the agreed wake-up time (e.g. at 2 AM in our example). Thedisclosure we have described above in FIGS. 1 to 5 provides theobjective measurements of sleep efficiency and combines with thesubjective user feedback to produce an SQOLI. At the end of the firstweek, the patient and therapist review the SQOLI measurements anddetermine the next step.

Step 4: Feedback loop based on Sleep Quality of Index. If the subjecthas achieved the desired SQOLI in the first week, then a new target isset. As a specific example, the subject will now go to bed 5.5 hoursbefore the agreed wake up time, but will still try to achieve the sametargets of 85% sleep efficiency and “difficulty falling asleep”metric<5. In subsequent weeks, the algorithm will be applied that theperson can increase their sleep time by 30 minutes, provided they havemet the targets in the previous week. This process can continue until afinal desired steady state sleep quality of life index is reached (e.g.,sleeping 7.5 hrs per night with a sleep efficiency of>85%).

The person skilled in the art will realize that a number of behavioralinterventions have been developed and described in the literature forimproving sleep quality. However a limitation of all these currentapproaches is that they do not have a reliable and easy means forproviding the sleep quality of life metric, and it is this limitationwhich the current disclosure overcomes. Furthermore, the person skilledin the art will also realize that a number of pharmaceuticalinterventions are appropriate for improvement of sleep quality (e.g.prescription of Ambien®), and that the disclosure described here cansupport these medical interventions also.

STATEMENT OF INDUSTRIAL APPLICABILITY

The apparatus, system and method of this disclosure finds utility incontactless and minimum contact assessment of quality-of-life indices inclinical and consumer trials, and in interventions to improve thequality of life.

1. A system for monitoring quality of life of a subject, comprising: aphysiological sensor unit configured to generate measured data based onreceived signals reflected from the subject; and instructions stored ina non-transitory computer readable storage medium that, when executed bya processor, cause the processor to: collect subjective data from thesubject, wherein the subjective data comprises sleep quality data; andcalculate a quality of life index on the basis of the measured data andsubjective data; wherein the quality of life index is used to generate arecommendation for the subject, the recommendation including informationrelated to at least one of use of medication and one or more behavioralinterventions for the subject.
 2. The system of claim 1, wherein therecommendation includes at least one of whether to use a medication anda dosage of the medication.
 3. The system of claim 1, wherein therecommendation is associated with providing a behavioral feedbackprogram to the subject.
 4. The system of claim 1, wherein the one ormore behavioral interventions includes a plurality of instruction stepsfor the monitored subject to achieve a set target quality of lifeparameter.
 5. The system of claim 4, wherein at least one of theinstruction steps includes an updated target quality of life parameterbased on the calculated quality of life index when the set targetquality of life parameter is reached.
 6. The system of claim 1, whereinthe non-transitory computer readable storage medium is in a portablehandheld device.
 7. The system of claim 1, wherein the measured datacomprises an objective measurement of sleep and the subjective datacomprises a subjective assessment of sleep, and wherein therecommendation relates to improving at least one aspect of the subject'ssleep.
 8. The system of claim 7, wherein the at least one aspect of thesubject's sleep is the subject's sleep efficiency and the subject'sdifficulty in falling asleep.
 9. The system of claim 1, furthercomprising a transmitter; and wherein the instructions further cause theprocessor to initially process the measured data and the subjectivedata, and the transmitter transmits the measured and subjective data toa remote data analysis system to calculate the quality of life index.10. The system of claim 9, wherein the remote data analysis system isconfigured to: receive the measured data and the subjective datatransmitted by the processor; calculate the quality of life index andgenerate the recommendation to be provided to the subject, based on thecalculated quality of life index; and transmit the recommendation to theprocessor.
 11. A method for monitoring the quality of life of a subject,the method comprising: receiving, at a handheld portable device,measured data from at least one physiological or environmental sensorunit; collecting, via an interface of the portable handheld device,subjective data from a subject; calculating, at the handheld portabledevice, a quality of life index as a function of at least the measureddata and the subjective data; and providing the quality of life indexfor generating a recommendation for the subject using the calculatedquality of life index, the recommendation including at least one ofinformation related to use of medication and one or more behavioralinterventions for the subject.
 12. The method of claim 11, wherein therecommendation includes at least one of whether to use a medication anda dosage of the medication.
 13. The method of claim 11, wherein therecommendation is associated with providing a behavioral feedbackprogram to the subject.
 14. The method of claim 11, wherein the one ormore behavioral interventions including a plurality of instruction stepsfor the monitored subject to achieve a set target quality of lifeparameter.
 15. The method of claim 14, wherein at least one of theinstruction steps includes an updated target quality of life parameterbased on the calculated quality of life index when the set targetquality of life parameter is reached.
 16. The method of claim 11,wherein the method further comprises: performing, at the handhelddevice, initial processing of the measured data and the subjective data;and transmitting the initially processed measured data and subjectivedata to a data analysis system for further processing; wherein thequality of life index is calculated as a function of at least theinitially processed measured data and subjective data.
 17. Anon-transitory computer readable storage medium on which readableinstructions of a program for a computing-device are stored, theinstructions, when executed by one or more computing devices, causingthe one or more computing devices to perform a method, the methodcomprising: receiving, at a handheld portable device, measured data fromat least one physiological or environmental sensor unit; collecting, viaan interface of the portable handheld device, subjective data from asubject; and calculating, at the handheld portable device, a quality oflife index as a function of at least the measured data and thesubjective data; wherein the quality of life index is provided forgenerating a recommendation for the subject using the calculated qualityof life index, the recommendation including at least one of informationrelated to use of medication and one or more behavioral interventionsfor the subject.
 18. The medium of claim 17, wherein the recommendationincludes at least one of whether to use a medication and a dosage of themedication.
 19. The medium of claim 17, wherein the recommendation isassociated with providing a behavioral feedback program to the subject.20. The medium of claim 17, wherein the one or more behavioralinterventions including a plurality of instruction steps for themonitored subject to achieve a set target quality of life parameter.